56,420 research outputs found
Classes of Terminating Logic Programs
Termination of logic programs depends critically on the selection rule, i.e.
the rule that determines which atom is selected in each resolution step. In
this article, we classify programs (and queries) according to the selection
rules for which they terminate. This is a survey and unified view on different
approaches in the literature. For each class, we present a sufficient, for most
classes even necessary, criterion for determining that a program is in that
class. We study six classes: a program strongly terminates if it terminates for
all selection rules; a program input terminates if it terminates for selection
rules which only select atoms that are sufficiently instantiated in their input
positions, so that these arguments do not get instantiated any further by the
unification; a program local delay terminates if it terminates for local
selection rules which only select atoms that are bounded w.r.t. an appropriate
level mapping; a program left-terminates if it terminates for the usual
left-to-right selection rule; a program exists-terminates if there exists a
selection rule for which it terminates; finally, a program has bounded
nondeterminism if it only has finitely many refutations. We propose a
semantics-preserving transformation from programs with bounded nondeterminism
into strongly terminating programs. Moreover, by unifying different formalisms
and making appropriate assumptions, we are able to establish a formal hierarchy
between the different classes.Comment: 50 pages. The following mistake was corrected: In figure 5, the first
clause for insert was insert([],X,[X]
Inference of termination conditions for numerical loops in Prolog
We present a new approach to termination analysis of numerical computations
in logic programs. Traditional approaches fail to analyse them due to non
well-foundedness of the integers. We present a technique that allows overcoming
these difficulties. Our approach is based on transforming a program in a way
that allows integrating and extending techniques originally developed for
analysis of numerical computations in the framework of query-mapping pairs with
the well-known framework of acceptability. Such an integration not only
contributes to the understanding of termination behaviour of numerical
computations, but also allows us to perform a correct analysis of such
computations automatically, by extending previous work on a constraint-based
approach to termination. Finally, we discuss possible extensions of the
technique, including incorporating general term orderings.Comment: To appear in Theory and Practice of Logic Programming. To appear in
Theory and Practice of Logic Programmin
Applying semantic web technologies to knowledge sharing in aerospace engineering
This paper details an integrated methodology to optimise Knowledge reuse and sharing, illustrated with a use case in the aeronautics domain. It uses Ontologies as a central modelling strategy for the Capture of Knowledge from legacy docu-ments via automated means, or directly in systems interfacing with Knowledge workers, via user-defined, web-based forms. The domain ontologies used for Knowledge Capture also guide the retrieval of the Knowledge extracted from the data using a Semantic Search System that provides support for multiple modalities during search. This approach has been applied and evaluated successfully within the aerospace domain, and is currently being extended for use in other domains on an increasingly large scale
Know2Look: Commonsense Knowledge for Visual Search
With the rise in popularity of social media, images accompanied by contextual text form a huge section of the web. However, search and retrieval of documents are still largely dependent on solely textual cues. Although visual cues have started to gain focus, the imperfection in object/scene detection do not lead to significantly improved results. We hypothesize that the use of background commonsense knowledge on query terms can significantly aid in retrieval of documents with associated images. To this end we deploy three different modalities - text, visual cues, and commonsense knowledge pertaining to the query - as a recipe for efficient search and retrieval
The generation of e-learning exercise problems from subject ontologies
The teaching/ learning of cognitive skills, such as
problem-solving, is an important goal in most forms of
education. In well-structured subject areas certain
exercise problem types may be precisely described by
means of machine-processable knowledge structures
or ontologies. These ontologies can readily be used to
generate individual problem examples for the student,
where each problem consists of a question and its
solution. An example is given from the subject domain
of computer databases
Generating Synthetic Data for Neural Keyword-to-Question Models
Search typically relies on keyword queries, but these are often semantically
ambiguous. We propose to overcome this by offering users natural language
questions, based on their keyword queries, to disambiguate their intent. This
keyword-to-question task may be addressed using neural machine translation
techniques. Neural translation models, however, require massive amounts of
training data (keyword-question pairs), which is unavailable for this task. The
main idea of this paper is to generate large amounts of synthetic training data
from a small seed set of hand-labeled keyword-question pairs. Since natural
language questions are available in large quantities, we develop models to
automatically generate the corresponding keyword queries. Further, we introduce
various filtering mechanisms to ensure that synthetic training data is of high
quality. We demonstrate the feasibility of our approach using both automatic
and manual evaluation. This is an extended version of the article published
with the same title in the Proceedings of ICTIR'18.Comment: Extended version of ICTIR'18 full paper, 11 page
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